Spectral Analysis of Jet Substructure with Neural Network: Boosted Higgs Case

At multi TeV pp colliders such as the LHC, boosted heavy particles can be produced and form a single collimated cluster of particles. Such a localized cluster is distinguished from QCD jets from quarks or gluons by the substructures of the cluster [1]. For this purpose, consistent definitions of substructures of jets have been studied extensively. There are various methods for identifying the jet substructures, such as strategies based on cluster decomposition [1-8] and shape variables [9-13]. These methods focus on different features of jet substructures to maximize the discrimination power. For the case of Higgs, W, and Z boson decaying hadronically into two quarks, a critical feature is a two-prong substructure inside. Because the key features depend on nature of the parent particle of a jet, there are several frameworks that can be applied to jets [14-18]. In this paper, we propose a new framework to identify jet substructures using a spectral function similar to the angular structure function [14, 19, 20]. Spectral analysis is widely used technique to explore quantum worlds.